DATA SCIENCE · CAREER TRACK

Ask better questions of data. Show your reasoning.

Explore data, examine uncertainty and build reproducible investigations. Connect statistical reasoning with practical analysis so your conclusion explains both the evidence and its limits.

Enquire about this track Is it for me?

₹30,000 per level including GST · Level 1 has no prerequisite · optional Career Audit

A portfolio you can explain

Customer behaviour investigation

Understand how activity differs between new and returning fictional customers.

Illustrative output: An EDA notebook and a qualified findings memo.

Explore the project briefs →

THE SHORT ANSWER

What is Data Science as a career track?

Data Science is a mentor-led career track. Its focus: Build and test statistical and predictive models, then explain what they show. First roles to search for include Data Science Trainee, Junior Data Scientist, Data Research Associate. Level 1 takes 3 months at ₹30,000 including GST, with no prerequisite. Coding: Python and notebook-based analysis.

WHO THIS TRACK SUITS

Is Data Science right for you?

People who enjoy framing a question, working carefully with data and explaining uncertainty, with the patience to learn statistics alongside Python.

Students: Suits students who like both numbers and writing. Your first project is an exploratory analysis with a clear question and honest limits.
Freshers: Prepares you for analyst roles first, with a path toward modelling as you progress. Entry-level 'Data Scientist' titles are uncommon; analyst work is the realistic start.
Working professionals: Fits analysts and domain experts who want to add statistical rigour and Python to decisions they already make.
Look elsewhere if: If you want to ship models and care most about validation, look at Machine Learning. If you want reporting and dashboards, look at Data Analytics.

Coding: Python and notebook-based analysis.
Mathematics: Probability, statistics and algebra, developed with practice.
Before you start: Practise Python functions, tabular data and plots. Review distributions, sampling and the difference between association and cause.

WHAT YOU WILL WORK ON

What will you learn in Data Science?

You learn these areas from recommended external courses; your mentor sets the practice and reviews the work.

Data exploration

Inspect structure, missingness, unusual values and the questions a dataset can answer.

Cleaning and analysis

Make transformations explicit and compare findings across relevant groups.

Visual investigation

Use charts to examine distributions and relationships, not just decorate a report.

Case studies and communication

Present a reproducible investigation with assumptions and limitations.

TOOLS AND SETUP

What tools do you need for Data Science?

Python (pandas, visualisation and statistics libraries) in a notebook, plus SQL for data access. Free to set up.

MENTORSHIP & REVISIONS

Bring your thinking to the review.

Inspect the question and sampling process, then the code. Examine missingness, confounding variables, uncertainty and whether the written conclusion goes beyond the available evidence.

See a sample review round →

LEVELS IN THIS TRACK

How long does Data Science take, and what does each level add?

Level 1 (3 months): small assignments and your first Data Science project, reviewed until it holds up. Level 2 (6 months): one larger project with revisions; resume review and mock interviews begin. Level 3 (6 months): a bigger project, portfolio guidance and application guidance for the roles you choose. Level 4 (6 months): a multi-part project with 1-on-1 reviews and system design and architecture review; aimed at working professionals switching direction. Every level is ₹30,000 including GST, bought one at a time.

What each level includes →

PRACTICE WITH PURPOSE

What projects will you build in Data Science?

Illustrative briefs set by mentors for practice, built on synthetic or appropriately licensed data. Your mentor sets your actual project to fit your level.

Illustrative project 01

Customer behaviour investigation

Understand how activity differs between new and returning fictional customers.

Sample inputs: Synthetic dated transaction records with a documented customer definition.
Your tasks: Build cohorts, examine missingness, visualise distributions and compare first-month behaviour.
What to deliver: An EDA notebook and a qualified findings memo.
Quality checks: Avoid comparing a full year's returning customers with one week of new customers; document observation windows.
Interview discussion: Explain how cohort definitions and survivorship affect the result.

Illustrative project 02

Delivery time analysis

Investigate variation in a fictional delivery service.

Sample inputs: Synthetic order times, route groups and delivery timestamps with deliberate missing entries.
Your tasks: Validate durations, inspect medians and tails, compare groups and propose what additional data would help.
What to deliver: A statistical report with reproducible charts.
Quality checks: Investigate impossible timestamps and selection bias; do not claim a route causes delay from association alone.
Interview discussion: Explain why one aggregate average hides the distribution users experience.

Illustrative project 03

Forecast investigation

Study whether historical demand helps estimate future activity.

Sample inputs: A synthetic daily series containing trend, seasonality and a documented unusual event.
Your tasks: Visualise the series, create time-based evaluation, compare a simple forecast and inspect residual patterns.
What to deliver: A forecasting notebook and a limitations presentation.
Quality checks: Keep future values out of training; report performance across multiple periods rather than one convenient week.
Interview discussion: Explain how an operational change can invalidate a historical pattern.

FIRST ROLES, HONESTLY

What jobs can Data Science lead to?

Entry-level Data Scientist roles are rare in India; the title usually follows analyst experience. The track prepares you for analyst roles where statistical thinking and clear write-ups are valued, with modelling as the next step.

Exploring datasets, preparing reproducible analysis and assisting statistical investigations. Consider data research associate or data science trainee positions; scientist roles often require deeper mathematical or domain preparation.

We do not guarantee eligibility, interviews or employment.

YOUR PORTFOLIO

Make your contribution visible.

A reproducible EDA notebook, a data dictionary, a statistical investigation and a concise findings presentation.

Career preparation by level All project briefs

PRACTISE THE CONVERSATION

What you will be asked about this work.

The questions attached to each project brief are the ones a mentor asks in review and an interviewer asks later. From Level 2, you rehearse them in mock interviews.

1
ABOUT “CUSTOMER BEHAVIOUR INVESTIGATION”
Explain how cohort definitions and survivorship affect the result.
2
ABOUT “DELIVERY TIME ANALYSIS”
Explain why one aggregate average hides the distribution users experience.
3
ABOUT “FORECAST INVESTIGATION”
Explain how an operational change can invalidate a historical pattern.

QUESTIONS ABOUT THIS TRACK

Before you choose Data Science.

How is this different from Machine Learning?

Data Science places broader emphasis on questions, exploration, statistics and communication. Machine Learning centres on model building and evaluation.

Can I skip statistics?

You can start with practical examples, but statistical reasoning is necessary to interpret uncertainty and avoid misleading conclusions.

What is EDA?

Exploratory data analysis is the process of inspecting a dataset, checking quality and investigating patterns before drawing conclusions.

Does correlation prove cause?

No. Confounding, selection and reverse causation can explain a relationship. Strong causal claims need a suitable design.

Should every project include a model?

No. A rigorous descriptive investigation may answer the question without predictive modelling.

How do I present a notebook professionally?

Make it runnable from the beginning, explain transformations and separate observations from interpretation.

What mathematics should I revise?

Start with distributions, conditional probability, sampling, averages and variation, then connect them to examples.

Which roles are realistic after initial study?

Trainee, research-support and analytics responsibilities can be relevant. Check each role's requirements rather than assuming a scientist title follows automatically.

KEEP EXPLORING